使用SVM模型对1万条中文评论文本进行健康与运动、积极情绪与鼓励、群体效应三类分类

以下是用SVM模型实现文本分类的Python代码示例，并计算准确率、召回率和F1得分：

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report

# 读取数据
data = pd.read_csv('comments.csv')  # 假设评论数据保存在comments.csv文件中
X = data['comment']  # 评论文本
y = data['category']  # 分类标签

# 文本向量化
vectorizer = TfidfVectorizer()
X_vectorized = vectorizer.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_vectorized, y, test_size=0.2, random_state=42)

# 构建SVM模型
svm = SVC()
svm.fit(X_train, y_train)

# 预测测试集
y_pred = svm.predict(X_test)

# 模型评估
accuracy = svm.score(X_test, y_test)
report = classification_report(y_test, y_pred)

# 打印评估结果
print('Accuracy:', accuracy)
print('Classification report:')
print(report)

请注意，上述代码中的数据文件路径和数据列名需要根据实际情况进行修改。